Perbandingan TextRank Berbasis TF-IDF dan Word2Vec dalam Peringkasan Teks Berita Bahasa Indonesia

Authors

  • Yohannes Christian Gurning Politeknik Negeri Medan
  • Samuel Cristian Saragih Politeknik Negeri Medan
  • Yuyun Yusnida Lase Politeknik Negeri Medan
  • Julham Julham Politeknik Negeri Medan

DOI:

https://doi.org/10.62712/juktisi.v4i2.552

Keywords:

Text Summarization, TF-IDF, Word2Vec, TextRank, ROUGE Score

Abstract

Automatic text summarization has become an essential solution for processing massive textual information, particularly in lengthy news articles. This study compares two variants of the TextRank algorithm using different weighting schemes: TF-IDF and Word2Vec, for summarizing Indonesian news texts. The dataset comprises 160 news articles from Kompas.com, which underwent preprocessing. Evaluation was conducted using ROUGE metrics (ROUGE-1, ROUGE-2, ROUGE-L), manual readability assessment, and execution runtime. The results indicate that TextRank with Word2Vec outperforms TF-IDF in both ROUGE scores (ROUGE-1 F1: 0.7033 vs 0.6454) and processing speed. These findings suggest that incorporating semantic representations into graph-based algorithms like TextRank significantly improves summary quality and runtime efficiency.

Downloads

Download data is not yet available.

References

Halimah, S. Agustian, dan S. Ramadhani, “Peringkasan teks otomatis (automated text summarization) pada artikel berbahasa Indonesia menggunakan algoritma LexRank,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 3, no. 3, 2022. [Online]. Tersedia: https://doi.org/10.37859/coscitech.v3i3.4300

N. Cholifah et al., “Pelatihan Citizen Journalism sebagai Upaya Penguatan Literasi Keamanan Digital,” DEDIKASI HUKUM Jurnal Pengabdian Kepada Masyarakat, vol. 4, no. 2, 2024.

A. P. Widyassari et al., “Review of automatic text summarization techniques & methods,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 4, pp. 1029–1046, 2022. [Online]. Tersedia: https://doi.org/10.1016/j.jksuci.2020.05.006

Y. Liliana et al., “Pengembangan Sistem Pemantauan Sentimen Berita Berbahasa Indonesia Berdasarkan Konten dengan Long Short-Term Memory,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 8, no. 4, 2021. [Online]. Tersedia: https://doi.org/10.25126/jtiik.202184624

P. D. Putri, D. Purwitasari, dan N. Suciati, “Eliminasi Data Non-Topik Menggunakan Pemodelan Topik untuk Peringkasan Otomatis Data Tweet dengan Konteks COVID-19,” Jurnal Teknologi Informasi, vol. 10, no. 2, 2021.

Pranoto dan L. Subhi, “Peringkasan Teks Otomatis (Automatic Text Summarization) pada Berita Online dengan Algoritma TextRank,” Skripsi S1, Universitas Malikussaleh, 2024.

N. Andrew, V. C. Mawardi, dan N. J. Perdana, “Implementasi Metode Word2Vec dan TextRank dalam Aplikasi Mobile Peringkas Berita Olahraga,” Jurnal Ilmu Komputer dan Sistem Informasi, 2024.

J. J. Sihombing, A. Arnita, S. I. Al Idrus, dan D. Y. Niska, “Implementation of text summarization on Indonesian scientific articles using TextRank algorithm with TF-IDF web-based,” Jurnal Soft Computing Exploration, vol. 5, no. 3, pp. 310–319, Des. 2024.

F. N. Dhewayani et al., “Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM,” Jurnal Teknologi dan Informasi, vol. 12, no. 1, 2022. [Online]. Tersedia: https://doi.org/10.34010/jati.v12i1

A. Tabassum dan R. R. Patil, “A Survey on Text Pre-Processing & Feature Extraction Techniques in Natural Language Processing,” International Research Journal of Engineering and Technology, vol. 7, no. 4, 2020. [Online]. Tersedia: www.irjet.net

A. Y. Siang, A. Setiawan, dan E. Alexander, “Pengujian Algoritma TextRank Dalam Merangkum Teks,” Prosiding Seminar Nasional AMIKOM Surakarta (SEMNASA), 2023.

R. A. Pranata, N. A. Verdikha, dan J. H. Ir Juanda, “Metode Pembobotan TF-IDF untuk Klasifikasi Teks Quick Count Pemilihan Wakil Presiden Indonesia 2024 pada X (Twitter) dengan Metode SVM,” Jurnal Teknologi Informasi, vol. 18, no. 2, pp. 126–135, 2024. [Online]. Tersedia: https://doi.org/10.47111/JTI

D. Septiani dan I. Isabela, “Analisis Term Frequency Inverse Document Frequency (TF-IDF) dalam Temu Kembali Informasi pada Dokumen Teks,” SINTESIA: Jurnal Sistem dan Teknologi Informasi Indonesia, vol. 6, no. 2, 2022.

I. A. Fathoni, “Implementasi Peringkasan Teks Otomatis dengan Algoritma TextRank untuk Berita Online,” Jurnal Sains dan Teknologi, vol. 5, no. 1, 2023.

T. Satvika dan J. Singh, “A Primer on Word Embedding,” dalam Handbook of Artificial Intelligence Techniques and Applications, Springer, 2021, pp. 525–541. [Online]. Tersedia: https://doi.org/10.1007/978-981-15-8530-2_42

Downloads

Published

2025-08-18

How to Cite

Gurning, Y. C., Saragih, S. C., Lase, Y. Y., & Julham, J. (2025). Perbandingan TextRank Berbasis TF-IDF dan Word2Vec dalam Peringkasan Teks Berita Bahasa Indonesia. Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI), 4(2), 922–928. https://doi.org/10.62712/juktisi.v4i2.552

Issue

Vol. 4 No. 2 (2025): September 2025

Section

Articles

License

Copyright (c) 2025 Yohannes Christian Gurning, Samuel Cristian Saragih, Yuyun Yusnida Lase, Julham Julham

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.